:py:mod:`airflow.providers.apache.spark.operators.spark_submit`
===============================================================

.. py:module:: airflow.providers.apache.spark.operators.spark_submit


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.apache.spark.operators.spark_submit.SparkSubmitOperator


.. py:class:: SparkSubmitOperator(*, application = '', conf = None, conn_id = 'spark_default', files = None, py_files = None, archives = None, driver_class_path = None, jars = None, java_class = None, packages = None, exclude_packages = None, repositories = None, total_executor_cores = None, executor_cores = None, executor_memory = None, driver_memory = None, keytab = None, principal = None, proxy_user = None, name = 'arrow-spark', num_executors = None, status_poll_interval = 1, application_args = None, env_vars = None, verbose = False, spark_binary = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   This hook is a wrapper around the spark-submit binary to kick off a spark-submit job.
   It requires that the "spark-submit" binary is in the PATH or the spark-home is set
   in the extra on the connection.

   .. seealso::
       For more information on how to use this operator, take a look at the guide:
       :ref:`howto/operator:SparkSubmitOperator`

   :param application: The application that submitted as a job, either jar or py file. (templated)
   :param conf: Arbitrary Spark configuration properties (templated)
   :param spark_conn_id: The :ref:`spark connection id <howto/connection:spark>` as configured
       in Airflow administration. When an invalid connection_id is supplied, it will default to yarn.
   :param files: Upload additional files to the executor running the job, separated by a
                 comma. Files will be placed in the working directory of each executor.
                 For example, serialized objects. (templated)
   :param py_files: Additional python files used by the job, can be .zip, .egg or .py. (templated)
   :param jars: Submit additional jars to upload and place them in executor classpath. (templated)
   :param driver_class_path: Additional, driver-specific, classpath settings. (templated)
   :param java_class: the main class of the Java application
   :param packages: Comma-separated list of maven coordinates of jars to include on the
                    driver and executor classpaths. (templated)
   :param exclude_packages: Comma-separated list of maven coordinates of jars to exclude
                            while resolving the dependencies provided in 'packages' (templated)
   :param repositories: Comma-separated list of additional remote repositories to search
                        for the maven coordinates given with 'packages'
   :param total_executor_cores: (Standalone & Mesos only) Total cores for all executors
                                (Default: all the available cores on the worker)
   :param executor_cores: (Standalone & YARN only) Number of cores per executor (Default: 2)
   :param executor_memory: Memory per executor (e.g. 1000M, 2G) (Default: 1G)
   :param driver_memory: Memory allocated to the driver (e.g. 1000M, 2G) (Default: 1G)
   :param keytab: Full path to the file that contains the keytab (templated)
   :param principal: The name of the kerberos principal used for keytab (templated)
   :param proxy_user: User to impersonate when submitting the application (templated)
   :param name: Name of the job (default airflow-spark). (templated)
   :param num_executors: Number of executors to launch
   :param status_poll_interval: Seconds to wait between polls of driver status in cluster
       mode (Default: 1)
   :param application_args: Arguments for the application being submitted (templated)
   :param env_vars: Environment variables for spark-submit. It supports yarn and k8s mode too. (templated)
   :param verbose: Whether to pass the verbose flag to spark-submit process for debugging
   :param spark_binary: The command to use for spark submit.
                        Some distros may use spark2-submit.

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['_application', '_conf', '_files', '_py_files', '_jars', '_driver_class_path', '_packages',...

      
   .. py:attribute:: ui_color
      

   .. py:method:: execute(context)

      Call the SparkSubmitHook to run the provided spark job


   .. py:method:: on_kill()

      Override this method to cleanup subprocesses when a task instance
      gets killed. Any use of the threading, subprocess or multiprocessing
      module within an operator needs to be cleaned up or it will leave
      ghost processes behind.